Nan Yang

Computer Vision and Pattern Recognition · with Cremers · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 318 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 103 citations into it; #10,303 of 21,575 by trust here, #12,172 by citations
• Aerospace Engineering — 62 citations into it; #2,231 of 6,421 by trust here, #2,734 by citations
• Media Technology — 16 citations into it
• Artificial Intelligence — 15 citations into it
• Geology — 13 citations into it
• Evaluation & Benchmarks — 9 citations into it
• 3D Vision & Pose — 8 citations into it
• Model Architectures — 8 citations into it

Whom Nan Yang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jakob J. Engel
2. Daniel Cremers
3. Kaiming He
4. Raul Mur-Artal
5. Diederik P. Kingma
6. Jürgen Sturm
7. Geoffrey E. Hinton
8. Alex Krizhevsky
9. Ross B. Girshick
10. David G. Lowe

Who trusts Nan Yang overall

1. Zachary Teed
2. Jia Deng
3. Sudeep Pillai
4. Chengzhou Tang
5. Rares Ambrus
6. Vitor Campanholo Guizilini
7. Ning Zhang
8. Ping Tan
9. Adrien Gaidon
10. Chaoqiang Zhao

Papers from Nan Yang's perspective

Ordered by how strongly the researchers Nan Yang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. ImageNet Large Scale Visual Recognition Challenge
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry (2020) — 464 citations
• Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry (2018) — 356 citations